PD-1 Binding Pocket Analysis

This repository contains the scripts and analysis files related to the detection and molecular dynamics (MD) simulation of binding pockets in the PD-1 (Programmed Cell Death Protein 1) structure. The study evaluates the druggability of identified pockets using structural descriptors and dynamic behavior from a 100 ns simulation trajectory.

🧬 Project Overview

Objective:
To identify and evaluate potential druggable pockets in the PD-1 immune checkpoint protein using Fpocket and GROMACS-based molecular dynamics simulations.

Key Highlights:

• Fpocket was used to detect potential binding pockets from the 2M2D PDB structure.
• MD simulations were performed using GROMACS 2024 with the OPLS-AA/L force field.
• Stability and druggability of pockets were evaluated using RMSD, RMSF, and SASA.
• Pocket 3 emerged as the most promising target for drug design based on its rigidity and induced-fit behavior.

📁 Repository Structure

PD-1-Binding-Pocket-Analysis/
├── data/                  # Raw output from Fpocket
├── md_files/              # GROMACS simulation files
├── scripts/               # Python scripts for analysis and plotting
├── figures/               # Generated plots (RMSD, RMSF, SASA, etc.)
└── README.md              # Project documentation

⚙️ Tools & Technologies

• Fpocket – Pocket detection: Fpocket GitHub
• GROMACS 2024 – Molecular dynamics simulations: GROMACS Website
• Python (pandas, matplotlib) – Data parsing, analysis, and visualization
• VMD – Structure visualization

📊 Key Results

• Pocket 1 has the highest volume and polarity but showed significant conformational instability.
• Pocket 3 maintained structural rigidity with low RMSD/RMSF values and compact SASA profiles.
• Dynamic analysis revealed Pocket 3 undergoes conformational "breathing," indicating induced-fit potential.

📂 How to Use

1. Pocket Detection:

   • Run Fpocket on the 2M2D.pdb file:

     fpocket -f 2M2D.pdb

2. Simulation Setup:

   • Prepare GROMACS input using gmx pdb2gmx, editconf, and solvate.
   • Neutralize the system and generate topol.top.

3. Run Molecular Dynamics:

   • Energy minimization, NVT, NPT equilibration, and 100 ns production run.

4. Analysis:

   • Use Python scripts in the scripts/ folder to extract RMSD, RMSF, SASA and plotting parameters over time.
   • Generate plots using:

     python scripts/RMSD_RMSF_SASA.ipynb
     python scripts/Plotting Parameter over time.ipynb

🧾 Citation

If you use this repository or dataset in your work, please cite: Panda, S. (2025) PD-1 Binding Pocket Analysis. GitHub repository. Available at: https://github.com/saspanda19/PD-1-Binding-Pocket-Analysis

📬 Contact

For questions or collaborations, feel free to reach out to:
📧 [email protected]
🔗 GitHub Profile

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License: MIT